The artificial intelligence (ai) in predictive toxicology global market report 2024 from The Business Research Company provides comprehensive market statistics, including global market size, regional shares, competitor market share, detailed segments, trends, and opportunities. This report offers an in-depth analysis of current and future industry scenarios, delivering a complete perspective for thriving in the industrial automation software market.
Artificial Intelligence (AI) In Predictive Toxicology Market, 2024 report by The Business Research Company offers comprehensive insights into the current state of the market and highlights future growth opportunities.
Market Size –
The artificial intelligence (AI) in predictive toxicology market size has grown exponentially in recent years. It will grow from $0.38 billion in 2023 to $0.50 billion in 2024 at a compound annual growth rate (CAGR) of 30.3%. The growth in the historic period can be attributed to increasing regulatory pressure for toxicity assessment, growing concerns about chemical safety, availability of large datasets for model training, rising costs and ethical concerns associated with animal testing, demand for faster and more cost-effective toxicity screening methods.
The artificial intelligence (AI) in predictive toxicology market size is expected to see exponential growth in the next few years. It will grow to $1.41 billion in 2028 at a compound annual growth rate (CAGR) of 29.9%. The growth in the forecast period can be attributed to emergence of explainable artificial intelligence (AI) in toxicology predictions, demand for personalized toxicity assessments, regulatory acceptance and standardization of AI-based toxicity predictions, rise in predictive toxicology outsourcing services, expansion of AI applications in drug discovery and development. Major trends in the forecast period include shift towards 3D cell culture models for toxicity testing, rise of federated learning approaches for collaborative toxicity prediction, application of generative models for predicting chemical toxicity pathways, incorporation of natural language processing for extracting toxicity data from literature, growth of virtual screening platforms for prioritizing chemical testing.
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The Business Research Company’s reports encompass a wide range of information, including:
1. Market Size (Historic and Forecast): Analysis of the market’s historical performance and projections for future growth.
2. Drivers: Examination of the key factors propelling market growth.
3. Trends: Identification of emerging trends and patterns shaping the market landscape.
4. Key Segments: Breakdown of the market into its primary segments and their respective performance.
5. Focus Regions and Geographies: Insight into the most critical regions and geographical areas influencing the market.
6. Macro Economic Factors: Assessment of broader economic elements impacting the market.
Artificial Intelligence (AI) In Predictive Toxicology Market Overview
Market Drivers –
The increasing demand for efficient and ethical drug discovery processes is expected to propel the growth of artificial intelligence (AI) in predictive toxicology market going forward. Drug discovery is an intricate and dynamic process that involves the systematic exploration, synthesis, and evaluation of chemical compounds to discover potential therapeutic agents to alleviate diseases and enhance overall well-being, while efficient and ethical drug discovery processes involve leveraging advanced technologies and prioritizing responsible research practices to develop new therapeutic agents. The demand for efficient and ethical drug discovery processes is driven by rising chronic diseases, growing trend towards personalized medicine, and an increased prevalence of age-related diseases. Artificial intelligence (AI) in predictive toxicology enhances drug discovery processes by efficiently analyzing vast datasets to predict potential toxicological outcomes, thereby accelerating drug development while ensuring ethical standards are met. For instance, in June 2023, according to the European Federation of Pharmaceutical Industries and Associations (EFPIA), a Belgium-based trade association representing the pharmaceutical industry, the total pharmaceutical R&D expenditure in Europe accounted for $49,500 million (€44,500 million) in 2022, an increase of approximately 6.45% from $46,500 million (€42,533 million) in 2021. Therefore, the increasing demand for efficient and ethical drug discovery processes is driving the growth of the artificial intelligence (AI) in predictive toxicology market.
Market Trends –
Major companies operating in the artificial intelligence (AI) predictive toxicology market are adopting a strategic collaboration approaches to enhance market presence and innovation. These collaborations aim to leverage AI and machine learning techniques to improve the prediction and assessment of the toxicity of chemical compounds and pharmaceuticals. For instance, in January 2024, SyntheticGestalt, a UK-based AI research and development company offering AI drug discovery solutions, partnered with Enamine, a Ukraine-based provider of chemical building blocks, screening compounds, and integrated drug discovery services, to develop a set of AI models facilitating the generation of synthetically accessible biologically active compounds, optimizing their physicochemical and ADME or Tox (Absorption, Distribution, Metabolism, and Excretion or Toxicity) properties. These models will find application in SyntheticGestalt’s compound discovery endeavors, catering to both academic users and pharmaceutical companies through their services. Enamine has made its extensive Enamine REAL database, containing 38 billion molecules, available for access. SyntheticGestalt will integrate this database into its Drug Discovery Service, utilizing proprietary AI models to predict the physicochemical and ADME or Tox properties of compounds. The service will promptly suggest improved alternative compounds for those with issues.
The artificial intelligence (AI) in predictive toxicology market covered in this report is segmented –
1) By Component: Solution, Services
2) By Technology: Machine Learning, Natural Language Processing, Computer Vision, Other Technologies
3) By Toxicity Endpoints: Genotoxicity, Hepatotoxicity, Neurotoxicity, Cardiotoxicity, Other Toxicity Endpoints
4) By End User: Pharmaceutical And Biotechnology Companies, Chemical And Cosmetics, Contract Research Organizations, Other End Users
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Regional Insights –
North America was the largest region in the artificial intelligence (AI) in predictive toxicology market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the artificial intelligence (AI) in predictive toxicology market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
Key Companies –
Major companies operating in the artificial intelligence (AI) in predictive toxicology market are Laboratory Corporation of America Holdings (LabCorp), Eurofins Scientific SE, Wuxi AppTec, Charles River Laboratories International, Medidata Solutions Inc., Certara Inc., Schrödinger, Merative L.P., Molecular Devices LLC, Envigo (Inotiv Inc.), Instem plc, Simulations Plus, Cyprotex, Recursion Pharmaceuticals, Exscientia PLC, BIOVIA Corporation, Lhasa Limited, Chemaxon Ltd., Insilico Medicine Inc., Algorithme Pharma, CiToxLAB, ArisGlobal, Benevolent AI, Berg Health, Cyclica Therapeutics Inc., Celsius Therapeutics, Biovista Inc., BioTeam Inc.
Table of Contents
1. Executive Summary
2. Artificial Intelligence (AI) In Predictive Toxicology Market Report Structure
3. Artificial Intelligence (AI) In Predictive Toxicology Market Trends And Strategies
4. Artificial Intelligence (AI) In Predictive Toxicology Market – Macro Economic Scenario
5. Artificial Intelligence (AI) In Predictive Toxicology Market Size And Growth
…..
27. Artificial Intelligence (AI) In Predictive Toxicology Market Competitor Landscape And Company Profiles
28. Key Mergers And Acquisitions
29. Future Outlook and Potential Analysis
30. Appendix
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